Joint Optimization Offloading Strategy of
Execution Time and Energy Consumption
of Mobile Edge
Computing
Qingzhu
Wang and Xiaoyun Cui
School of Computer
Science, Northeast Electric Power University, China
Abstract: As mobile devices
become more and more powerful, applications generate a large number of
computing tasks, and mobile devices themselves cannot meet the needs of users.
This article proposes a computation offloading model in which execution units
including mobile devices, edge server, and cloud server. Previous studies on
joint optimization only considered tasks execution time and the energy
consumption of mobile devices, and ignored the energy consumption of edge and
cloud server. However, edge server and cloud server energy consumption have a
significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three
execution units, and formulates task offloading decision as a single-objective
optimization problem. Genetic algorithm with elitism
preservation and random strategy is adopted to obtain optimal solution of the
problem. At last, simulation experiments show that the proposed computation
offloading model has lower fitness value compared with other computation
offloading models.
Keyword: Energy
consumption, execution time, mobile edge computing, offloading strategy.
Received July 15, 2020; accepted March 10, 2021
https://doi.org/10.34028/iajit/18/5/11